At the 3rd National Digital Innovation Summit 2026, the panel discussion on “AI, IT & Electronics Manufacturing: Best Practices from States Driving India’s Digital Industrial Growth” brought together policymakers, industry leaders, technology experts, and academia to examine how Artificial Intelligence is reshaping India’s manufacturing landscape. The discussion explored the growing role of smart factories, industrial AI, robotics, semiconductors, predictive maintenance, IoT-enabled manufacturing, and advanced computing in strengthening industrial productivity and competitiveness.
The conversation moved beyond AI as a standalone technology to examine its role across the entire industrial value chain, from supply chains and production planning to quality control, asset maintenance, logistics and workforce development. The panel also examined the policy and ecosystem requirements for Industry 4.0 adoption, particularly among MSMEs, the development of semiconductor and electronics manufacturing, startup commercialisation, predictive maintenance in critical infrastructure, and the need for industry-ready talent. A recurring theme was the importance of combining technology with human expertise, policy support, skills, and responsible innovation.
Jaspreet Singh, Director Industries-cum-State Startup Nodal Officer and Managing Director, Punjab Infotech
Artificial Intelligence is creating opportunities to improve almost every stage of industrial operations, but its impact depends significantly on the policy environment in which technologies are deployed. Jaspreet Singh brought the state-government perspective to the discussion, focusing on the role of policy in accelerating Industry 4.0 adoption across both MSMEs and large enterprises. Data generated through industries, digital transactions, and public systems can become a valuable resource for AI-led decision-making, provided there is a robust policy framework governing its collection, use, protection, and analysis.
Punjab is also working to strengthen its startup and innovation ecosystem through its Industrial Business Development Policy 2026 and initiatives such as the Punjab Investor Circle. The approach extends beyond conventional seed funding, with greater attention to taking startups from proof of concept to commercialisation. Milestone-based interventions, incubation support and opportunities for startups to secure initial orders can help bridge the gap between innovation and market adoption. With incubators operating across academic and industry ecosystems, the focus is increasingly on creating a pathway where promising ideas can be tested, refined, commercialised and eventually scaled.
Dr. Arvind Bhisikar, Executive Director (IT), Indian Ports Association
The transformation of India’s industrial economy also depends on modernising the infrastructure that supports trade and logistics. Dr. Arvind Bhisikar described the evolution towards Port 4.0, where digital platforms, AI, sensors and advanced technologies are being integrated into increasingly complex port ecosystems. Ports bring together shipping companies, customs, railways, immigration, port health authorities and several other stakeholders, making seamless information exchange essential for efficient cargo movement.
Digital platforms such as the Port Community System and Maritime Single Window, including Sagar Setu, are enabling greater integration between these stakeholders. AI and sensor-based technologies are also finding applications within port operations. One example is the use of Vessel Traffic Management Systems (VTMS) and indigenous radar technologies to improve monitoring and management of vessel movement. Such systems demonstrate how electronics, sensors, and digital platforms can work together to strengthen critical infrastructure.
Deepu Shyam, Executive Director, RailTel
In large-scale infrastructure such as railways, equipment failure can have consequences far beyond a single asset. Deepu Shyam explained how the traditional approach of schedule-based maintenance, where assets are inspected or serviced at predetermined intervals, can leave organisations exposed to unexpected failures and operational downtime.
The combination of IoT sensors, real-time data and AI-based analytics offers a more predictive approach. Sensors can continuously capture parameters such as voltage, current, temperature, and other asset conditions. AI models can then analyse these patterns to identify potential failures before they occur, allowing maintenance teams to intervene proactively. This can significantly reduce unplanned downtime and improve asset availability.
Hitesh Vaidya, Director – Commercial Business, Acer India
The growing adoption of AI is creating demand for a new generation of computing devices and infrastructure capable of supporting AI workloads. Hitesh Vaidya placed this transformation in the context of the wider economic impact of AI, noting that its value will ultimately be measured by its ability to improve productivity and economic output. The increasing availability of AI-enabled devices is already reflecting this shift, with AI PCs accounting for a growing share of the market.
The manufacturing ecosystem itself is undergoing a similar transformation. Acer’s manufacturing operations in India, including its captive facility in Puducherry and partnerships with contract manufacturers, are increasingly using AI-enabled applications for quality management and workflow optimisation. The company has also expanded into AI server manufacturing, reflecting the growing demand for computing infrastructure required by the AI economy.
Prof. Raj Kumar Mittal, Vice Chancellor, Babasaheb Bhimrao Ambedkar University, Lucknow
The transition towards AI-driven manufacturing ultimately depends on the availability of skilled and adaptable human capital. Prof. Raj Kumar Mittal brought the academic perspective to the discussion, describing AI as a technology with applications across virtually every sector of the economy. For India to strengthen manufacturing competitiveness and advance towards the vision of a developed economy, greater adoption of AI must be accompanied by systematic investment in education and skills.
At Babasaheb Bhimrao Ambedkar University, Lucknow, efforts include establishing a Centre of Excellence in Artificial Intelligence and progressively integrating AI into academic programmes. The objective is not simply to create technical specialists but to develop professionals who combine technological knowledge with ethical understanding and human capabilities. This becomes particularly important as AI increasingly influences workplace decision-making and industrial processes.
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Key Takeaways
- AI is becoming an industrial productivity engine, influencing supply chains, production, quality management and asset maintenance.
- Industry 4.0 adoption among MSMEs requires affordable, low-risk solutions, particularly sensor-based retrofitting of existing machinery.
- Predictive maintenance can reduce downtime by using IoT sensors and AI models to identify potential equipment failures in advance.
- Smart ports and logistics are using digital platforms, predictive analytics, sensors and AI to improve cargo and vessel management.
- India’s semiconductor ecosystem needs not only fabs but also strong ancillary industries, component suppliers and supporting technologies.
- AI-ready computing infrastructure is becoming increasingly important as enterprises and manufacturers adopt AI at scale.
- Startup commercialisation requires support beyond seed funding, including milestone-based interventions, incubation and access to initial markets.
- Policy frameworks are critical for governing data, enabling investment and creating an environment conducive to industrial AI adoption.
- Universities and industry need stronger collaboration to develop professionals equipped for AI-driven manufacturing.
- Human-machine integration will define the next generation of smart manufacturing, with AI and robotics working alongside human expertise rather than simply replacing it.
Conclusion: Building an Intelligent and Inclusive Manufacturing Ecosystem
The panel discussion made it clear that India’s next phase of industrial growth will be shaped by the convergence of AI, electronics, advanced computing, IoT, robotics, and data-driven manufacturing. These technologies are already moving into practical applications, including predictive maintenance in railways, intelligent port operations, AI-enabled quality management, and smart manufacturing environments.
However, technological adoption alone will not create a globally competitive manufacturing ecosystem. Policy support, affordable technology, semiconductor supply chains, intellectual property, startup commercialisation and industry-ready skills must develop alongside AI capabilities. For MSMEs in particular, the pathway to Industry 4.0 will need to focus on practical and cost-effective solutions that allow existing infrastructure to become smarter rather than obsolete.
Ultimately, the next generation of Indian manufacturing will be defined by human-machine integration, where AI improves productivity, robotics handles increasingly complex tasks, IoT creates real-time visibility and people continue to provide creativity, judgement and innovation. Building this ecosystem at scale can strengthen India’s position not only as a global manufacturing destination, but as a developer and exporter of the technologies powering the next industrial revolution.